ALS

Alternating Least Squares (ALS) matrix factorization.

Input

It takes in a DataFrame as input and performs ALS

Output

It generates the ALSModel and passes it to the next Predict and ModelSave Nodes. It also passes the incoming DataFrame to the next Nodes

Type

ml-estimator

Class

fire.nodes.ml.NodeALS

Fields

Name	Title	Description
userCol	User Column	The column name for user ids.
itemCol	Item Column	The column name for item ids.
ratingCol	Rating Column	The column name for ratings.
predictionCol	Prediction Column	The prediction column created during model scoring
maxIter	Max iterations	The maximum number of iterations.
regParam	Regularization Param	The regularization parameter.(>=0)
alpha	Alpha	The alpha parameter in the implicit preference formulation.(>=0)
checkpointInterval	Checkpoint Interval	The checkpoint interval.
nonnegative	Non negative	Whether to apply nonnegativity constraints.
numItemBlocks	Num Item Blocks	The number of item blocks.
numUserBlocks	Num User Blocks	The number of user blocks.
rank	Rank	The rank of the matrix factorization.
seed	Seed	Random Seed.
implicitPrefs	Implicit Prefs	whether to use implicit preference

Details

Collaborative filtering is commonly used for recommender systems. These techniques aim to fill in the missing entries of a user-item association matrix. spark.mllib currently supports model-based collaborative filtering, in which users and products are described by a small set of latent factors that can be used to predict missing entries. spark.mllib uses the alternating least squares (ALS) algorithm to learn these latent factors.

More at Spark MLlib/ML docs page : http://spark.apache.org/docs/latest/mllib-collaborative-filtering.html